kdtree.h

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#pragma once
 
#include <pcl/search/search.h>
#include <pcl/kdtree/kdtree_flann.h>
 
namespace pcl
{
  // Forward declarations
  template <typename T> class PointRepresentation;
 
  namespace search
  {
    /** \brief @b search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search 
      * functions using KdTree structure. KdTree is a generic type of 3D spatial locator using kD-tree structures. 
      * The class is making use of the FLANN (Fast Library for Approximate Nearest Neighbor) project 
      * by Marius Muja and David Lowe.
      *
      * \author Radu B. Rusu
      * \ingroup search
      */
    template<typename PointT, class Tree = pcl::KdTreeFLANN<PointT> >
    class KdTree: public Search<PointT>
    {
      public:
        using PointCloud = typename Search<PointT>::PointCloud;
        using PointCloudConstPtr = typename Search<PointT>::PointCloudConstPtr;
 
        using pcl::search::Search<PointT>::indices_;
        using pcl::search::Search<PointT>::input_;
        using pcl::search::Search<PointT>::getIndices;
        using pcl::search::Search<PointT>::getInputCloud;
        using pcl::search::Search<PointT>::nearestKSearch;
        using pcl::search::Search<PointT>::radiusSearch;
        using pcl::search::Search<PointT>::sorted_results_;
 
        using Ptr = shared_ptr<KdTree<PointT, Tree> >;
        using ConstPtr = shared_ptr<const KdTree<PointT, Tree> >;
 
        using KdTreePtr = typename Tree::Ptr;
        using KdTreeConstPtr = typename Tree::ConstPtr;
        using PointRepresentationConstPtr = typename PointRepresentation<PointT>::ConstPtr;
 
        /** \brief Constructor for KdTree. 
          *
          * \param[in] sorted set to true if the nearest neighbor search results
          * need to be sorted in ascending order based on their distance to the
          * query point
          *
          */
        KdTree (bool sorted = true); 
 
        /** \brief Destructor for KdTree. */
        
        ~KdTree () override = default;
 
        /** \brief Provide a pointer to the point representation to use to convert points into k-D vectors. 
          * \param[in] point_representation the const boost shared pointer to a PointRepresentation
          */
        void
        setPointRepresentation (const PointRepresentationConstPtr &point_representation);
 
        /** \brief Get a pointer to the point representation used when converting points into k-D vectors. */
        inline PointRepresentationConstPtr
        getPointRepresentation () const
        {
          return (tree_->getPointRepresentation ());
        }
 
        /** \brief Sets whether the results have to be sorted or not.
          * \param[in] sorted_results set to true if the radius search results should be sorted
          */
        void 
        setSortedResults (bool sorted_results) override;
        
        /** \brief Set the search epsilon precision (error bound) for nearest neighbors searches.
          * \param[in] eps precision (error bound) for nearest neighbors searches
          */
        void
        setEpsilon (float eps);
 
        /** \brief Get the search epsilon precision (error bound) for nearest neighbors searches. */
        inline float
        getEpsilon () const
        {
          return (tree_->getEpsilon ());
        }
 
        /** \brief Provide a pointer to the input dataset.
          * \param[in] cloud the const boost shared pointer to a PointCloud message
          * \param[in] indices the point indices subset that is to be used from \a cloud 
          */
        bool
        setInputCloud (const PointCloudConstPtr& cloud, 
                       const IndicesConstPtr& indices = IndicesConstPtr ()) override;
 
        /** \brief Search for the k-nearest neighbors for the given query point.
          * \param[in] point the given query point
          * \param[in] k the number of neighbors to search for
          * \param[out] k_indices the resultant indices of the neighboring points (must be resized to \a k a priori!)
          * \param[out] k_sqr_distances the resultant squared distances to the neighboring points (must be resized to \a k
          * a priori!)
          * \return number of neighbors found
          */
        int
        nearestKSearch (const PointT &point, int k, 
                        Indices &k_indices,
                        std::vector<float> &k_sqr_distances) const override;
 
        /** \brief Search for all the nearest neighbors of the query point in a given radius.
          * \param[in] point the given query point
          * \param[in] radius the radius of the sphere bounding all of p_q's neighbors
          * \param[out] k_indices the resultant indices of the neighboring points
          * \param[out] k_sqr_distances the resultant squared distances to the neighboring points
          * \param[in] max_nn if given, bounds the maximum returned neighbors to this value. If \a max_nn is set to
          * 0 or to a number higher than the number of points in the input cloud, all neighbors in \a radius will be
          * returned.
          * \return number of neighbors found in radius
          */
        int
        radiusSearch (const PointT& point, double radius, 
                      Indices &k_indices,
                      std::vector<float> &k_sqr_distances,
                      unsigned int max_nn = 0) const override;
      protected:
        /** \brief This leaves the internal tree_ object uninitialized! */
        KdTree (const std::string& name, bool sorted);
        /** \brief A pointer to the internal KdTree object. */
        KdTreePtr tree_;
    };
  }
}
 
#ifdef PCL_NO_PRECOMPILE
#include <pcl/search/impl/kdtree.hpp>
#else
#define PCL_INSTANTIATE_KdTree(T) template class PCL_EXPORTS pcl::search::KdTree<T>;
#endif